Overview

Dataset statistics

Number of variables16
Number of observations19877
Missing cells7042
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory128.0 B

Variable types

NUM10
CAT6

Warnings

name has a high cardinality: 19459 distinct values High cardinality
host_name has a high cardinality: 3351 distinct values High cardinality
neighbourhood has a high cardinality: 128 distinct values High cardinality
last_review has a high cardinality: 1345 distinct values High cardinality
last_review has 3513 (17.7%) missing values Missing
reviews_per_month has 3513 (17.7%) missing values Missing
price is highly skewed (γ1 = 38.46497002) Skewed
minimum_nights is highly skewed (γ1 = 34.5447924) Skewed
name is uniformly distributed Uniform
id has unique values Unique
number_of_reviews has 3513 (17.7%) zeros Zeros
availability_365 has 2136 (10.7%) zeros Zeros

Reproduction

Analysis started2021-03-04 09:20:25.570132
Analysis finished2021-03-04 09:20:45.234926
Duration19.66 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIQUE

Distinct19877
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24448050.07
Minimum6499
Maximum48142332
Zeros0
Zeros (%)0.0%
Memory size155.3 KiB
2021-03-04T09:20:45.467305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6499
5-th percentile2139754.4
Q114099690
median24475545
Q335592480
95-th percentile45214944.8
Maximum48142332
Range48135833
Interquartile range (IQR)21492790

Descriptive statistics

Standard deviation13365214.48
Coefficient of variation (CV)0.5466781376
Kurtosis-1.04555167
Mean24448050.07
Median Absolute Deviation (MAD)10735242
Skewness-0.07734771589
Sum4.859538913e+11
Variance1.786289581e+14
MonotocityStrictly increasing
2021-03-04T09:20:45.864146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
366673921< 0.1%
 
372852231< 0.1%
 
417086581< 0.1%
 
437965761< 0.1%
 
150849111< 0.1%
 
5994061< 0.1%
 
36905951< 0.1%
 
393868621< 0.1%
 
81647121< 0.1%
 
297943211< 0.1%
 
Other values (19867)1986799.9%
 
ValueCountFrequency (%) 
64991< 0.1%
 
256591< 0.1%
 
292481< 0.1%
 
293961< 0.1%
 
299151< 0.1%
 
ValueCountFrequency (%) 
481423321< 0.1%
 
481396461< 0.1%
 
481360171< 0.1%
 
481359431< 0.1%
 
481326401< 0.1%
 

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct19459
Distinct (%)97.9%
Missing10
Missing (%)0.1%
Memory size155.3 KiB
BR Guest House
 
11
Brand New Hostel in center of Lisbon.
 
10
Quinta da Bicuda - Estúdio Bungalow
 
9
Amazing 1 Bedroom apartment
 
9
Quarto com WC privativo - Arrendamento mensal
 
8
Other values (19454)
19820 
ValueCountFrequency (%) 
BR Guest House110.1%
 
Brand New Hostel in center of Lisbon.100.1%
 
Quinta da Bicuda - Estúdio Bungalow9< 0.1%
 
Amazing 1 Bedroom apartment9< 0.1%
 
Quarto com WC privativo - Arrendamento mensal8< 0.1%
 
RCGI Homesweet in Lisboa7< 0.1%
 
West Coast Surf Hostel6< 0.1%
 
One Bedroom Apartment6< 0.1%
 
NLC Rooms & Suites,made by Travelers for Travelers6< 0.1%
 
Limmo Garden Guest House - Private Suite6< 0.1%
 
Other values (19449)1978999.6%
 
(Missing)100.1%
 
2021-03-04T09:20:46.680892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique19198 ?
Unique (%)96.6%
2021-03-04T09:20:47.083327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length132
Median length34
Mean length34.46324898
Min length1

host_id
Real number (ℝ≥0)

Distinct8715
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102741377.2
Minimum14455
Maximum387871064
Zeros0
Zeros (%)0.0%
Memory size155.3 KiB
2021-03-04T09:20:47.448365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14455
5-th percentile1756107
Q115191207
median63244098
Q3174887261
95-th percentile312536974
Maximum387871064
Range387856609
Interquartile range (IQR)159696054

Descriptive statistics

Standard deviation103107284
Coefficient of variation (CV)1.003561436
Kurtosis-0.2604225337
Mean102741377.2
Median Absolute Deviation (MAD)57755799
Skewness0.9213568162
Sum2.042190355e+12
Variance1.063111202e+16
MonotocityNot monotonic
2021-03-04T09:20:47.820391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
39531092761.4%
 
17561071100.6%
 
1040839741000.5%
 
7564916900.5%
 
257927631820.4%
 
1969293800.4%
 
76223539780.4%
 
22192546600.3%
 
5691663590.3%
 
2372087570.3%
 
Other values (8705)1888595.0%
 
ValueCountFrequency (%) 
144551< 0.1%
 
170961< 0.1%
 
514611< 0.1%
 
688051< 0.1%
 
709331< 0.1%
 
ValueCountFrequency (%) 
3878710641< 0.1%
 
3875067211< 0.1%
 
3873805301< 0.1%
 
3870063681< 0.1%
 
3868420631< 0.1%
 

host_name
Categorical

HIGH CARDINALITY

Distinct3351
Distinct (%)16.9%
Missing6
Missing (%)< 0.1%
Memory size155.3 KiB
Maria
 
460
Ana
 
407
Pedro
 
346
Feels Like Home
 
276
João
 
263
Other values (3346)
18119 
ValueCountFrequency (%) 
Maria4602.3%
 
Ana4072.0%
 
Pedro3461.7%
 
Feels Like Home2761.4%
 
João2631.3%
 
Joana2341.2%
 
Luis2211.1%
 
Ricardo2171.1%
 
Nuno1991.0%
 
Sofia1840.9%
 
Other values (3341)1706485.8%
 
2021-03-04T09:20:48.290603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1565 ?
Unique (%)7.9%
2021-03-04T09:20:48.677570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length6
Mean length8.312622629
Min length1
Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size155.3 KiB
Lisboa
14100 
Cascais
1873 
Sintra
 
1254
Mafra
 
1210
Lourinh
 
355
Other values (11)
 
1085
ValueCountFrequency (%) 
Lisboa1410070.9%
 
Cascais18739.4%
 
Sintra12546.3%
 
Mafra12106.1%
 
Lourinh3551.8%
 
Oeiras2891.5%
 
Torres Vedras2591.3%
 
Loures1300.7%
 
Amadora1170.6%
 
Odivelas760.4%
 
Other values (6)2141.1%
 
2021-03-04T09:20:49.074519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-04T09:20:49.279444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length6
Mean length6.212506918
Min length5

neighbourhood
Categorical

HIGH CARDINALITY

Distinct128
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size155.3 KiB
Santa Maria Maior
3170 
Misericrdia
2378 
Arroios
1763 
Cascais e Estoril
1304 
So Vicente
1217 
Other values (123)
10045 
ValueCountFrequency (%) 
Santa Maria Maior317015.9%
 
Misericrdia237812.0%
 
Arroios17638.9%
 
Cascais e Estoril13046.6%
 
So Vicente12176.1%
 
Santo Antnio11735.9%
 
Estrela8334.2%
 
Ericeira7213.6%
 
Avenidas Novas6453.2%
 
S.Maria, S.Miguel, S.Martinho, S.Pedro Penaferrim5502.8%
 
Other values (118)612330.8%
 
2021-03-04T09:20:49.453978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)< 0.1%
2021-03-04T09:20:49.610560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length49
Median length12
Mean length13.8796096
Min length3

latitude
Real number (ℝ≥0)

Distinct8428
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.75915047
Minimum38.67645
Maximum39.29767
Zeros0
Zeros (%)0.0%
Memory size155.3 KiB
2021-03-04T09:20:49.758163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum38.67645
5-th percentile38.69913
Q138.71094
median38.7175
Q338.73953
95-th percentile38.991854
Maximum39.29767
Range0.62122
Interquartile range (IQR)0.02859

Descriptive statistics

Standard deviation0.1092921508
Coefficient of variation (CV)0.002819776735
Kurtosis8.729100488
Mean38.75915047
Median Absolute Deviation (MAD)0.00938
Skewness2.961007627
Sum770415.6338
Variance0.01194477422
MonotocityNot monotonic
2021-03-04T09:20:49.915743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
38.71308220.1%
 
38.7385210.1%
 
38.71212210.1%
 
38.73643210.1%
 
38.73046200.1%
 
38.7113200.1%
 
38.72384200.1%
 
38.71161200.1%
 
38.71367200.1%
 
38.71272190.1%
 
Other values (8418)1967399.0%
 
ValueCountFrequency (%) 
38.676453< 0.1%
 
38.676621< 0.1%
 
38.67861< 0.1%
 
38.678971< 0.1%
 
38.679151< 0.1%
 
ValueCountFrequency (%) 
39.297671< 0.1%
 
39.297041< 0.1%
 
39.29681< 0.1%
 
39.296411< 0.1%
 
39.296191< 0.1%
 

longitude
Real number (ℝ)

Distinct9891
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.206918109
Minimum-9.49852
Maximum-8.84009
Zeros0
Zeros (%)0.0%
Memory size155.3 KiB
2021-03-04T09:20:50.103242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-9.49852
5-th percentile-9.429526
Q1-9.26749
median-9.14702
Q3-9.13485
95-th percentile-9.123028
Maximum-8.84009
Range0.65843
Interquartile range (IQR)0.13264

Descriptive statistics

Standard deviation0.1133625657
Coefficient of variation (CV)-0.01231275921
Kurtosis-0.2998830159
Mean-9.206918109
Median Absolute Deviation (MAD)0.01604
Skewness-1.131796479
Sum-183005.9113
Variance0.0128510713
MonotocityNot monotonic
2021-03-04T09:20:50.268123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-9.13535260.1%
 
-9.14246240.1%
 
-9.13214200.1%
 
-9.13155190.1%
 
-9.15084180.1%
 
-9.1477170.1%
 
-9.13503160.1%
 
-9.13508160.1%
 
-9.15036160.1%
 
-9.13782160.1%
 
Other values (9881)1968999.1%
 
ValueCountFrequency (%) 
-9.498521< 0.1%
 
-9.488081< 0.1%
 
-9.487891< 0.1%
 
-9.48291< 0.1%
 
-9.482511< 0.1%
 
ValueCountFrequency (%) 
-8.840091< 0.1%
 
-8.862851< 0.1%
 
-8.867751< 0.1%
 
-8.868091< 0.1%
 
-8.869481< 0.1%
 

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size155.3 KiB
Entire home/apt
14725 
Private room
4369 
Hotel room
 
414
Shared room
 
369
ValueCountFrequency (%) 
Entire home/apt1472574.1%
 
Private room436922.0%
 
Hotel room4142.1%
 
Shared room3691.9%
 
2021-03-04T09:20:50.458614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-04T09:20:50.568320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:50.713369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length14.16219751
Min length10

price
Real number (ℝ≥0)

SKEWED

Distinct494
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.24812597
Minimum0
Maximum20199
Zeros1
Zeros (%)< 0.1%
Memory size155.3 KiB
2021-03-04T09:20:50.850970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q140
median60
Q394
95-th percentile230
Maximum20199
Range20199
Interquartile range (IQR)54

Descriptive statistics

Standard deviation260.0588293
Coefficient of variation (CV)2.730330142
Kurtosis2270.250473
Mean95.24812597
Median Absolute Deviation (MAD)25
Skewness38.46497002
Sum1893247
Variance67630.5947
MonotocityNot monotonic
2021-03-04T09:20:51.005558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
508664.4%
 
607673.9%
 
406143.1%
 
455913.0%
 
305152.6%
 
705102.6%
 
805072.6%
 
655072.6%
 
1004892.5%
 
554832.4%
 
Other values (484)1402870.6%
 
ValueCountFrequency (%) 
01< 0.1%
 
83< 0.1%
 
9370.2%
 
10510.3%
 
11360.2%
 
ValueCountFrequency (%) 
201991< 0.1%
 
99992< 0.1%
 
85781< 0.1%
 
81251< 0.1%
 
80003< 0.1%
 

minimum_nights
Real number (ℝ≥0)

SKEWED

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.797504654
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Memory size155.3 KiB
2021-03-04T09:20:51.201081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum1000
Range999
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16.30213814
Coefficient of variation (CV)4.292855343
Kurtosis1731.116252
Mean3.797504654
Median Absolute Deviation (MAD)1
Skewness34.5447924
Sum75483
Variance265.759708
MonotocityNot monotonic
2021-03-04T09:20:51.362650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2702735.4%
 
1533326.8%
 
3439522.1%
 
410035.0%
 
57503.8%
 
74642.3%
 
302431.2%
 
61520.8%
 
151320.7%
 
28780.4%
 
Other values (46)3001.5%
 
ValueCountFrequency (%) 
1533326.8%
 
2702735.4%
 
3439522.1%
 
410035.0%
 
57503.8%
 
ValueCountFrequency (%) 
10002< 0.1%
 
7301< 0.1%
 
4003< 0.1%
 
3653< 0.1%
 
3641< 0.1%
 

number_of_reviews
Real number (ℝ≥0)

ZEROS

Distinct429
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.8716104
Minimum0
Maximum802
Zeros3513
Zeros (%)17.7%
Memory size155.3 KiB
2021-03-04T09:20:51.534192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q355
95-th percentile187
Maximum802
Range802
Interquartile range (IQR)53

Descriptive statistics

Standard deviation67.66015947
Coefficient of variation (CV)1.57820429
Kurtosis9.628760081
Mean42.8716104
Median Absolute Deviation (MAD)13
Skewness2.671232755
Sum852159
Variance4577.89718
MonotocityNot monotonic
2021-03-04T09:20:51.687819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0351317.7%
 
113486.8%
 
29394.7%
 
37103.6%
 
45812.9%
 
54822.4%
 
64062.0%
 
73781.9%
 
83501.8%
 
93051.5%
 
Other values (419)1086554.7%
 
ValueCountFrequency (%) 
0351317.7%
 
113486.8%
 
29394.7%
 
37103.6%
 
45812.9%
 
ValueCountFrequency (%) 
8021< 0.1%
 
7271< 0.1%
 
6951< 0.1%
 
6811< 0.1%
 
6261< 0.1%
 

last_review
Categorical

HIGH CARDINALITY
MISSING

Distinct1345
Distinct (%)8.2%
Missing3513
Missing (%)17.7%
Memory size155.3 KiB
2021-01-02
 
247
2021-01-03
 
160
2021-01-01
 
158
2020-03-15
 
151
2020-01-02
 
146
Other values (1340)
15502 
ValueCountFrequency (%) 
2021-01-022471.2%
 
2021-01-031600.8%
 
2021-01-011580.8%
 
2020-03-151510.8%
 
2020-01-021460.7%
 
2021-01-311340.7%
 
2020-01-011310.7%
 
2020-03-161230.6%
 
2020-01-031040.5%
 
2020-03-131020.5%
 
Other values (1335)1490875.0%
 
(Missing)351317.7%
 
2021-03-04T09:20:51.884545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique397 ?
Unique (%)2.4%
2021-03-04T09:20:52.055539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length8.762841475
Min length3

reviews_per_month
Real number (ℝ≥0)

MISSING

Distinct604
Distinct (%)3.7%
Missing3513
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean1.159753728
Minimum0.01
Maximum44.75
Zeros0
Zeros (%)0.0%
Memory size155.3 KiB
2021-03-04T09:20:52.205137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.24
median0.72
Q31.73
95-th percentile3.62
Maximum44.75
Range44.74
Interquartile range (IQR)1.49

Descriptive statistics

Standard deviation1.250472079
Coefficient of variation (CV)1.078222082
Kurtosis93.84222368
Mean1.159753728
Median Absolute Deviation (MAD)0.57
Skewness4.101096497
Sum18978.21
Variance1.56368042
MonotocityNot monotonic
2021-03-04T09:20:52.347547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.053271.6%
 
0.062801.4%
 
0.092541.3%
 
0.072451.2%
 
0.162441.2%
 
0.112041.0%
 
0.032031.0%
 
0.171971.0%
 
0.11780.9%
 
0.131740.9%
 
Other values (594)1405870.7%
 
(Missing)351317.7%
 
ValueCountFrequency (%) 
0.01290.1%
 
0.021230.6%
 
0.032031.0%
 
0.041660.8%
 
0.053271.6%
 
ValueCountFrequency (%) 
44.751< 0.1%
 
15.551< 0.1%
 
12.491< 0.1%
 
11.421< 0.1%
 
9.851< 0.1%
 

calculated_host_listings_count
Real number (ℝ≥0)

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.23122202
Minimum1
Maximum276
Zeros0
Zeros (%)0.0%
Memory size155.3 KiB
2021-03-04T09:20:52.494157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile48
Maximum276
Range275
Interquartile range (IQR)9

Descriptive statistics

Standard deviation35.47728739
Coefficient of variation (CV)2.681331123
Kurtosis39.58234138
Mean13.23122202
Median Absolute Deviation (MAD)2
Skewness5.96889533
Sum262997
Variance1258.63792
MonotocityNot monotonic
2021-03-04T09:20:52.629792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1576629.0%
 
2248212.5%
 
317018.6%
 
413086.6%
 
59955.0%
 
69124.6%
 
76023.0%
 
85042.5%
 
104702.4%
 
94412.2%
 
Other values (40)469623.6%
 
ValueCountFrequency (%) 
1576629.0%
 
2248212.5%
 
317018.6%
 
413086.6%
 
59955.0%
 
ValueCountFrequency (%) 
2762761.4%
 
1101100.6%
 
1001000.5%
 
90900.5%
 
82820.4%
 

availability_365
Real number (ℝ≥0)

ZEROS

Distinct366
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.7686774
Minimum0
Maximum365
Zeros2136
Zeros (%)10.7%
Memory size155.3 KiB
2021-03-04T09:20:52.791359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1122
median278
Q3361
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)239

Descriptive statistics

Standard deviation133.0285016
Coefficient of variation (CV)0.5690604193
Kurtosis-1.132030803
Mean233.7686774
Median Absolute Deviation (MAD)87
Skewness-0.6048257352
Sum4646620
Variance17696.58223
MonotocityNot monotonic
2021-03-04T09:20:52.945947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
365270113.6%
 
0213610.7%
 
36414987.5%
 
1804772.4%
 
3634342.2%
 
1793861.9%
 
453141.6%
 
3202811.4%
 
3622671.3%
 
902661.3%
 
Other values (356)1111755.9%
 
ValueCountFrequency (%) 
0213610.7%
 
11520.8%
 
2200.1%
 
3170.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
365270113.6%
 
36414987.5%
 
3634342.2%
 
3622671.3%
 
3611580.8%
 

Interactions

2021-03-04T09:20:27.051455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:27.218011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:27.382572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:27.536160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:27.682766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:27.848325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:28.004905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:28.178442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:28.315220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:28.464334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:28.609456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:28.778004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:28.975477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-04T09:20:29.933439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:30.098000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:30.258571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:30.447067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:30.597663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:31.078569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:31.238760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:31.398335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:31.561153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:31.712394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:31.879283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:32.034235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:32.188821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:32.351386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:32.505973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:32.671529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:32.828111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:32.982697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:33.151896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:33.303521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:33.460064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:33.604534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:33.754404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:33.917187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-04T09:20:34.303617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:34.491114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-04T09:20:34.851151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:35.041642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:35.218174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:35.400281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:35.581067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:35.756575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:35.899194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-04T09:20:41.879330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-04T09:20:42.960449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:43.129659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:43.289733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:43.461312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:43.625834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:43.775432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-04T09:20:53.363843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-04T09:20:53.602210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-04T09:20:53.846566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-04T09:20:54.096409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-04T09:20:54.328775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-04T09:20:44.094609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:44.526498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:44.821033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-04T09:20:44.972627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
06499Belém 1 Bedroom Historical Apartment14455BrunoLisboaBelm38.69750-9.19768Entire home/apt403272021-01-260.341341
125659Heart of Alfama - Lisbon Center107347EllieLisboaSanta Maria Maior38.71167-9.12696Entire home/apt30101132019-12-081.361108
229248Apartamento Alfama com vista para o rio!125768BárbaraLisboaSanta Maria Maior38.71272-9.12628Entire home/apt3833252021-01-102.641303
329396Alfama Hill - Boutique apartment126415MónicaLisboaSanta Maria Maior38.71156-9.12987Entire home/apt2522652021-01-222.492323
429915Modern and Cool Apartment in Lisboa128890SaraLisboaAvenidas Novas38.74712-9.15286Entire home/apt485402021-01-240.311294
533348Happy Season144484BrunoLisboaLumiar38.76381-9.15256Private room40122011-07-220.0220
642519Nice Apart.BAIRRO ALTO (ADAMASTOR) 6-1º136230DavidLisboaMisericrdia38.70896-9.14938Entire home/apt5011142020-03-081.0011258
748025Apartment for renting in Lisbon218778JoséLisboaMisericrdia38.71309-9.14392Entire home/apt655182020-09-100.155365
848058Small House Downtown Cascais218990PimCascaisCascais e Estoril38.69650-9.42571Entire home/apt805332020-07-230.461218
948854Comfortable 4BR in historical villa, near market222551DagmarSintraS.Maria, S.Miguel, S.Martinho, S.Pedro Penaferrim38.80380-9.37970Entire home/apt1615452020-12-230.6710

Last rows

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
1986748085251GuestReady - Cozy Black Bricked Flat in Vibrant Graça Quarters341831718JoaoLisboaArroios38.72597-9.13108Entire home/apt3610NaNNaN1365
1986848099900Lovely 2 Bedroom Duplex Apt w/Terrace in Cascais387871064MárciaCascaisAlcabideche38.71688-9.42668Entire home/apt6420NaNNaN1178
1986948100008Lisboa Lovely Apartment Bairro Alto10058743RentportugalLisboaMisericrdia38.71195-9.14374Entire home/apt20212021-02-121.012269
1987048121569Casa centro cascais1827516Karla LamounierCascaisCascais e Estoril38.69895-9.42395Entire home/apt39210NaNNaN373
1987148130409Moradia T3 nos Jardins da Parede | Jardim19254511GoncaloCascaisCascais e Estoril38.70380-9.39386Entire home/apt90300NaNNaN2364
1987248132640Sky Room27809636MiguelMafraEriceira38.96054-9.41491Private room8010NaNNaN5365
1987348135943Hera 1 By Innkeeper203773123InnkeeperLisboaMisericrdia38.71197-9.14780Entire home/apt3320NaNNaN31359
1987448136017Hera 2 By Innkeeper203773123InnkeeperLisboaMisericrdia38.71038-9.14749Entire home/apt3320NaNNaN31365
1987548139646Dom Durão Village House, no sopé de Montejunto335615367CarlotaCadavalLamas e Cercal39.22942-9.07565Entire home/apt3220NaNNaN2364
1987648142332Guincho Countryside bedroom with private bathroom38921144PedroCascaisAlcabideche38.73939-9.43694Private room2430NaNNaN2364